Sample processing improvements for quantitative microscopy

ABSTRACT

Among other things, a diluted sample is generated based on mixing a small sample of blood with a one or more diluents. A thin film of the diluted sample is formed on the surface of a contact optical microscopy sensor. Red blood cells within a portion of the thin film of the diluted sample are illuminated using light of a predetermined wavelength. One or more images of the diluted sample are acquired based on illuminating the red blood cells within the portion of the thin film of the diluted sample. The acquired one or more images of the diluted sample are then processed. The mean corpuscular hemoglobin in the red blood cells within the portion of the thin film of the diluted sample is determined based on processing the acquired images of the diluted sample.

PRIORITY CLAIMS AND RELATED APPLICATIONS

This application is a continuation and claims priority to U.S. patentapplication Ser. No. 15/066,065, filed Mar. 10, 2016, which is entitledto the benefit of the filing date of U.S. patent application 62/131,164,filed Mar. 10, 2015 and is a continuation-in-part of U.S. patentapplication Ser. No. 14/173,500, filed Feb. 5, 2014. This application isalso a continuation-in-part of U.S. patent application Ser. No.14/314,743, filed Jun. 25, 2014 (issued as U.S. Pat. No. 9,518,920 onDec. 13, 2016), and is entitled to the benefit of the filing date ofU.S. patent application 61/839,735, filed Jun. 26, 2013. Each of thepatent applications identified above is incorporated here by referencein its entirety.

This application is also related to U.S. patent application Ser. No.14/698,532, filed on Apr. 28, 2015, which claims the benefit of U.S.patent application Ser. No. 12/913,639 filed on Oct. 27, 2010 (nowissued as U.S. Pat. No. 9,041,790 on May 26, 2015), which claims thebenefit of U.S. provisional patent application 61/255,781, filed on Oct.28, 2009 and is also related to U.S. patent application Ser. No.14/710,046, filed on May 12, 2015, which claims benefit to U.S. patentapplication Ser. No. 13/095,175 filed on Apr. 27, 2011 (now issued asU.S. Pat. No. 9,075,225 on Jul. 7, 2015), which claims the benefit ofSer. No. 12/913,639, filed on Oct. 27, 2010 (now issued as U.S. Pat. No.9,041,790 on May 26, 2015), which claims the benefit of U.S. provisionalpatent application 61/255,781, filed on Oct. 28, 2009. Each of thepatent applications identified above is incorporated here by referencein its entirety.

FIELD

This specification generally describes technology related to sampleprocessing for quantitative microscopy.

BACKGROUND

Complete blood count (CBC) and other diagnostic tests that measurehemoglobin (Hb) content of blood typically measure Hb via aspectroscopic system or a subsystem after lysing red blood cells (RBCs).

SUMMARY

Traditional techniques that measure Hb content of blood often add bulkand complexity to the measurement processing, creating a number ofdisadvantages that are salient for point-of-care (POC) diagnosticdevices. For instance, such techniques often require significant samplepreparation, which is often inaccessible in resource-limited regionswhere POC diagnostic devices are often used.

In general, in an aspect, a method for computing mean corpuscularhemoglobin can include: generating a diluted sample based on mixing asmall sample of blood with one or more diluents; forming a thin film ofthe diluted sample on a surface of a contact optical microscopy sensor;illuminating red blood cells within a portion of the thin film of thediluted sample using light of a predetermined wavelength; acquiring oneor more images of the diluted sample based on illuminating the red bloodcells within the portion of the thin film of the diluted sample;processing the acquired one or more images of the diluted sample; anddetermining a value of mean corpuscular hemoglobin in the red bloodcells within the portion of the thin film of the diluted sample based onprocessing the acquired images of the diluted sample.

One or more implementations may include the following optional features.For example, in some implementations, forming a thin film includesforming a thin film between a transparent chamber lid and the surface ofthe contact optical microscopy sensor.

In some implementations, forming a thin film between the lid and thecontact optical microscopy sensor includes: placing the diluted sampleon a surface of the contact optical microscopy sensor; and lowering thetransparent chamber lid to a predetermined height determined by aspacer.

In some implementations, the size of the predetermined height isconfigured such that lowering of the transparent chamber lid onto thesurface of the contact optical microscopy sensor (i) constrains the redblood cells to lie within a broadest dimension of the red blood cellsparallel to the surface of the contact optical microscopy sensor, and(ii) does not result in structural damage to the red blood cells.

In some implementations, the one or more images of the diluted samplethat are acquired include image features of at least one hundred redblood cells.

In some implementations, the predetermined wavelength comprises awavelength that corresponds to a wavelength within the absorbance bandof a form of hemoglobin with the highest extinction coefficient of thatform of hemoglobin.

In some implementations, processing the acquired images of the dilutedsample includes: estimating a background pixel value for each pixelwithin the respective acquired images; segmenting one or more regionswithin the respective acquired images that each contain exactly one redblood cell; and calculating the mean corpuscular hemoglobin based on thesegmented one or more regions within the respective acquired images.

In some implementations, the generated diluted sample: has anisotonicity that is substantially equal to an isotonicity of red bloodcells; has coagulation properties such that the generated diluted sampleis less likely to coagulate compared to coagulation properties of redblood cells; and maintains a predetermined pH level of the generateddiluted sample.

In some implementations, the acquired one or more images include atleast a statistically significant number of the red blood cells in thediluted sample.

In some implementations, at least one of the one or more diluentscomprises a nitrite.

In some implementations, generating a diluted sample comprisesgenerating a diluted sample based on mixing the sample of blood with adiluent such that the mixing results in sphering of the red blood cellswithin the diluted sample.

In general, in an aspect, a method for computing a mean amount of ameasured analyte within a fluid sample can include: forming a thin filmof a fluid sample on a surface of a contact optical microscopy sensor,the fluid sample comprising particulate matter that includes an analyteto be measured, the analyte having a distinctive absorption spectrum;illuminating at least a portion of the thin film of the fluid sampleusing white light of a predetermined wavelength; acquiring one or moreimages of the fluid sample based on illuminating at least a portion ofthe thin film of the fluid sample using white light of a predetermined;wavelength; processing the acquired one or more images of the fluidsample; and determining a value of a mean amount of the analyte based onprocessing the one or more images of the fluid sample.

In some implementations, forming a thin film includes forming a thinfilm between a transparent chamber lid and the contact opticalmicroscopy sensor.

In some implementations, forming a thin film between the lid and thesurface of the contact optical microscopy sensor includes: placing thefluid sample on a surface of the contact optical microscopy sensor andlowering the transparent chamber lid to a predetermined heightdetermined by a spacer.

In some implementations, the size of the predetermined height isconfigured such that lowering of the transparent chamber lid onto thesurface of the contact optical microscopy sensor (i) constrains theparticulate matter of the sample fluid to lie within a broadestdimension of the particular matter to the surface of the contact opticalmicroscopy sensor, and (ii) does not result in structural damage to theparticulate matter of the sample fluid.

In some implementations, processing the one or more acquired imagesincludes: estimating a background pixel value for each pixel within therespective one or more acquired images; segmenting one or more regionswithin the respective acquired images that each contain exactly oneparticle; and calculating the mean amount of the analyte based on thesegmented one or more regions within the respective acquired images.

In some implementations, forming a thin film includes: placing the fluidsample on the surface of the contact optical microscopy sensor; andenabling the thin film of the fluid sample formed on a surface of acontact optical microscopy sensor to settle.

These, and other aspects, features, implementations, and advantages maybe expressed as methods, apparatus, systems, components, compositions,software products, methods of doing business, and in other ways.

These and other aspects, features, implementations, and advantages willbecome apparent from the following description and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is graph of hemoglobin absorption spectra.

FIG. 2 is a contact optical microscopy image of a blood cell.

In the drawings, like reference numbers represent corresponding partsthroughout.

DETAILED DESCRIPTION

In some examples, the bulk and complexity of the conventional CBCprocess can be overcome with the use of contact optical microscopy(COM)-based microspectrometry. In some instances, such techniques canalso be implemented with optical lens microscopes. In COM-basedmicrospectrometry, molecules in individual pixels and regions in amicroscopic image are quantified using optical absorption measurements.COM-based microspectrometry permits measurement of the Hb content ofindividual RBCs within an image. This can then be averaged over aplurality of imaged RBCs to estimate the mean corpuscular hemoglobin(MCH). The estimation of MCH, along with mean corpuscular volume (MCV)and red blood cell concentration (RBC), both of which can be determinedfrom the microscopy image, mean corpuscular hemoglobin concentration(MCHC) and concentration of hemoglobin in blood (Hgb), additionalelements of the CBC, can then be derived.

To calculate the MCH from a COM image, microspectrometry must be used toinitially quantify Hb content. Microspectrometry within lens-basedmicroscopy has existed for many decades and has been performedpreviously on RBCs. In 1960, Sondhaus & Thorell published a study inwhich the Hb absorbance spectra from sub-areas of RBCs were examined,exploring the correlation of RBC maturity and Hb and free-iron content[C. A. Sondhaus and B. Thorell, “Microspectrophotometric Determinationof Nonheme Iron in Maturing Erythroblasts and its Relationship to theEndocellular Hemoglobin Formation,” Blood, vol. 16, no. 3, pp. 1285-97,1960]. Tsujita et al. measured the change of the absorbance spectrumwithin RBCs depending the presence of nitric oxide [K. Tsujita, T.Shiraishi, and K. Kakinuma, “Microspectrophotometry of nitricoxide-dependent changes in hemoglobin in single red blood cellsincubated with stimulated macrophages,” J. Biochem., vol. 122, no. 2,pp. 264-70, August 1997]. Meletis et al. attempted to measure MCH usinglens-based transmission microspectrometry [J. Meletis, X. Yataganas, G.Eliopoulos, J. Panourgais, D. Loukopoulos, and P. Fessas, “HemoglobinContent of Single Erythrocytes from Fetuses with Parents HavingHeterozyhous ß-Thalassemia,” Acta Haematol., vol. 73, pp. 16-21, 1985].Blood was spread on a glass slide, and 50-100 RBCs in each sample weremeasured by illuminating them with 415 nm light, dividing the full areaof each RBC into 0.5 μm pixels, and measuring absorbance at each pixel.This process was slow (60-90 minutes per test), and results, though wellcorrelated with those obtained using ordinary instruments based onspectroscopy of lysed blood (R²=0.87) were unacceptably variable(coefficient of variation, CV=28%).

Some of this variability is due to the small number of RBC within astandard high magnification field of view. COM, however, yields highmagnification over a very large field of view that can include largenumbers of RBCs. In addition, lens-based microspectrometry forquantification of absorbing molecules is also sensitive to focal planealignment relative to the RBCs, since light transmitting through Hb thatis outside the focal plane may not be collected. Thus, for example, thedepth of focus of the objective lens used by Meletis et al. was <0.5 μm,which is much thinner than an RBC (typically 1.1 μm thick at the centerand 2.6 μm thick at the torus-shaped outer band [K. G. Engstrom and E.Löfvenberg, “Treatment of Myeloproliferative Disorders With Hydroxyurea:Effects on Red Blood Cell Geometry and Deformability,” Blood, vol. 91,no. 10, pp. 3986-3991, 1998]), meaning parts of every RBC measured wereout of focus, so that part of the Hb signal can be blurred outside thecell borders and lost. COM, on the other hand, has no focal plane—COMproduces very little blurring of objects less than a few microns fromthe imaging surface—so can outperform standard microspectrometry for MCHmeasurement.

Standard optical spectrometry measures the quantity of a light-absorbinganalyte dispersed in a solution using Beer's law:

$\begin{matrix}{\frac{I}{I_{0}} = {10^{\bigwedge}\left( {{- \epsilon}\; C\;\ell} \right)}} & (1)\end{matrix}$

where I₀ is the incident light intensity, I is the transmitted lightintensity, ∈ is the extinction coefficient (molar absorptivity) of theabsorber (Hb in this case) at the incident wavelength, C is the molarconcentration of the absorber in the solution, and

0 is the path length through the solution. Extinction is caused by lighteither being scattered or lost to true absorption, where the energy isconverted to another form. Microspectrometry relies on Beer's law, butapplies it to images of microscale objects to calculate the absorber'sconcentration within that object.

The following derivation of the relationship between MCH and Beer's lawdepends on the following simplifying assumptions:

-   -   1. The cell in question causes no reflection, refraction, or        scattering.    -   2. Only one Hb form is present, meaning E is known.    -   3. There is uniform [Hb] concentration inside the RBC and        negligible [Hb] outside the RBC.    -   4. Within a given pixel, the RBC thickness,        , is uniform.

The validity of these assumptions is addressed below.

For each pixel covered by an RBC, the following two equations aredefined:

$\begin{matrix}{C = {\lbrack{Hb}\rbrack = \frac{{Hb}_{p}}{V}}} & (2) \\{V = {d^{2}*\ell}} & (3)\end{matrix}$

where Hb_(p) is the number of moles of Hb in the column above the pixel,V is the volume of the RBC portion directly above the pixel, and d isthe pixel side-length. The 2^(nd) equation assumes that

is constant over the entire pixel. These two definitions are substitutedinto Beer's law to yield:

$\begin{matrix}{\frac{I_{p}}{I_{0,p}} = {10^{\bigwedge}\left( {{- {\epsilon\left( \frac{{Hb}_{p}}{d^{2}\ell} \right)}}\ell} \right)}} & (4)\end{matrix}$

where I_(p) is the measured intensity of the pixel and I_(0,p) is theestimated background intensity, i.e., the intensity expected if no RBCwas present. The above equation is not sensitive to

, which is useful because path length is not known. The [Hb] term hasalso been eliminated, which too is not directly measureable in a COMimage. Equation (4) is rearranged, yielding

$\begin{matrix}{{\log\frac{I_{p}}{I_{0,p}}} = {{- \epsilon}\frac{{Hb}_{p}}{d^{2}}}} & (5)\end{matrix}$

Converting moles of Hb to mass of Hb via a simple unit conversion,m_(Hb,p)=M_(Hb)*Hb_(p), where M_(Hb) is the molar mass, and solving form_(Hb,p) yields the mass of Hb above the pixel.

$\begin{matrix}{m_{{Hb},p} = {\frac{M_{Hb}d^{2}}{\epsilon}{\log\left( \frac{I_{0,p}}{I_{p}} \right)}}} & (6)\end{matrix}$

This is summed over all the pixels in the RBC, yielding

$\begin{matrix}{m_{{Hb},{RBC}} = {\sum\limits_{p = 1}^{P}{\frac{M_{Hb}d^{2}}{\epsilon}{\log\left( \frac{I_{0,p}}{I_{p}} \right)}}}} & (7)\end{matrix}$

where P is the total number of pixels covered by the RBC. Oncem_(Hb,RBC) is calculated for N RBCs in the image, the mean is taken toyield the MCH:

$\begin{matrix}{{MCH} = {\frac{M_{Hb}d^{2}}{N\;\epsilon}{\sum\limits_{n = 1}^{N}{\sum\limits_{p = 1}^{P_{n}}{\log\left( \frac{I_{0,{np}}}{I_{np}} \right)}}}}} & (8)\end{matrix}$

In the MCH equation above, M_(Hb), d, and E are known constants. I_(np),I_(0,np), P_(n), and N and are outputs from computer vision describedbelow.

To increase the signal-to-noise ratio of the intensity measurements, itis preferable to illuminate Hb at a wavelength where its extinctioncoefficient is as high as possible. This gives maximum separation of Iand I₀, maximizing signal. The absorbance maxima for Hb occur in theviolet region of the visible spectrum (400-430 nm).

As depicted in FIG. 1, different varieties of Hb have differentabsorption spectra and maxima. Oxygenated hemoglobin (oxy-Hb) anddeoxygenated hemoglobin (deoxy-Hb) both exist in blood. The bloodoxygenation of Hb, i.e., the percentage of Hb in oxy-Hb form, can varyin the human body from 60% (venous blood) to ˜100% (arterial blood). Thevast majority of the remaining Hb exists in deoxy-Hb form, except incases of blood poisoning. The MCH_(COM) could depend on the oxygenationpercentage and thus differ from the true MCH.

Though oxy- and deoxy-Hb have different absorbance profiles, it wassuspected that in a real POC test, the vast majority of the Hb would beoxygenated while the blood droplet waited on a finger. If it is assumedthat Hb is fully oxygenated, then ∈_(oxy-Hb) has a maximum value of5.243*10⁵ cm⁻¹M⁻¹, occurring at 415 nm. Substituting this value intoEquation (8), as well as M_(Hb)=64 500 g/mol [S. Prahl, “OpticalAbsorption of Hemoglobin,” Oregon Medical Laser Center, 1999. [Online].Available: http://omlc.org/spectra/hemoglobin/. [Accessed: 15 Feb.2013].] and d=1.1 μm (the pixel size of the COM sensor used in thisresearch) yields:

$\begin{matrix}{{MCH} = {\frac{1.49}{N}{\sum\limits_{n = 1}^{N}{\sum\limits_{p = 1}^{P_{n}}{\log\left( \frac{I_{0,{np}}}{I_{np}} \right)}}}}} & (9)\end{matrix}$

Note that the illumination wavelength distribution can alter thiscoefficient. LEDs commonly have full-width-half-maxima of 5 nm and 10nm, whereas the extinction peaks have full-width-half-maxima of ˜20 nm.As a result, using the extinction maximum is a reasonable approximation,even if the LED wavelength peak is off by 2 nm or so. Apart fromimproving the absorbance signal, a second advantage of selecting theextinction maximum is that the signal is less sensitive to smalldeviations in LED wavelength, since the slope at the peak is 0 (thoughthe drop-off away from the peak is steep, so this effect is limited). Asample COM image obtained using 415 nm illumination is depicted in FIG.2.

As depicted in FIG. 2, violet illumination results in good contrast forRBCs in comparison to platelets and WBCs. Contrast also aids inmachine-based RBC segmentation and counting, since RBCs are easilydistinguished from other objects in the image.

It is assumed in the above analysis that all Hb is converted to oxy-Hb.If this is not the case, the MCH correlation is likely to be poor.However, there are different ways to address this potential problem.

One solution is to illuminate the blood at the maximum oxy/deoxy-Hbisosbestic point, 422 nm. Isosbestic points are wavelengths where bothforms of Hb have equal absorbance. However, LEDs are not easily found atthis wavelength. To predict the effect of off-isosbestic illumination,the ratio of measured milt, to true milt, can be calculated, where themeasured milt, is calculated under the assumption of full oxygenation.The m_(Hb,measured) is inversely proportional to ∈_(Hb), as seen inEquation (6), meaning that the mass measurement of any deoxy Hb will beoff by a factor of ∈_(deoxy-Hb)/∈_(oxy-Hb), the ratio of true extinctionto presumed extinction. To calculate the mass ratio, the mass fractionsof oxy-Hb and deoxy-Hb should be added together but with the deoxy-Hbfraction multiplied by the ratio of the extinction coefficients:

$\begin{matrix}{\frac{m_{{Hb},{measured}}}{m_{{Hb},{true}}} = {f + {\frac{\epsilon_{{deoxy} - {Hb}}}{\epsilon_{{oxy} - {Hb}}}\left( {1 - f} \right)}}} & (10)\end{matrix}$

where f is the oxy-Hb fraction, set to 0.6 in the worst case. Convertingthe mass ratio to a percent difference is straightforward, and theresult is obtained that if the illumination is even 1 nm off theisosbestic point in either direction, the resulting Hb mass measurementwill off be up to 4%. This is due to the sharply opposing slopes of theoxy/deoxy-Hb absorbance. A wavelength tolerance of <1 nm may beachievable with appropriate illumination sources and optical filters,but a simpler solution is available, as described below.

There are other isosbestic points with less severe differences inslopes, such as 390 nm, but these have much lower absorbance, reducingRBC contrast. They may be useful, but would be more difficult from acomputer-vision perspective.

Another possibility is to chemically convert both oxy-Hb and deoxy-Hb toa third, single form. StP instruments typically convert all Hb tocarboxyhemoglobin (CO-Hb) or methemoglobin (met-Hb) after lysing theRBCs. For Hb microspectrometry, Hb needs to be converted within thecell, without damaging the membrane, during a reasonably shortincubation time. Fortunately, sodium nitrite (NaNO₂) is a suitableconversion agent. Sodium nitrite can pass through the RBC membrane, andit converts both oxy- and deoxy-Hb to met-Hb. Met-Hb is hemoglobin whoseiron ions have an oxidation state of 3+, instead of the normal 2+.Oxygen does not bind to met-Hb. Met-Hb has an absorbance maximum at 405nm with a 10-20% higher extinction coefficient compared to oxy-Hb [C.Donadee, N. J. H. Raat, T. Kanias, J. Tejero, J. S. Lee, E. E. Kelley,X. Zhao, C. Liu, H. Reynolds, I. Azarov, S. Frizzell, E. M. Meyer, A. D.Donnenberg, L. Qu, D. Triulzi, D. B. Kim-Shapiro, and M. T. Gladwin,“Nitric oxide scavenging by red blood cell microparticles and cell-freehemoglobin as a mechanism for the red cell storage lesion,” Circulation,vol. 124, no. 4, pp. 465-76, July 2011]. Assuming the maximum extinctioncoefficient for met-Hb is 15% higher than that of oxyHb, the MCHequation becomes:

$\begin{matrix}{{MCH} = {\frac{1.28}{N}{\sum\limits_{n = 1}^{N}{\sum\limits_{p = 1}^{P_{n}}{\log\left( \frac{I_{0,{np}}}{I_{np}} \right)}}}}} & (11)\end{matrix}$

Blood and Power [30] showed that conversion of both oxy-Hb and deoxy-Hbto met-Hb in live RBCs is also relatively rapid, particularly when bothoxy-Hb and deoxy-Hb species exist in the blood: maximum [met-Hb] wasreached after 20 minutes with an initial 1:12 nitrite:heme ratio (hemebeing the oxygen binding subunit of Hb, of which there are four permolecule), and was followed by [metHb] decreasing slowly due tomethemoglobin reductase activity and reaction with nitric oxide, thebyproduct of methemylation of deoxy-Hb [A. B. Blood and G. G. Power, “Invitro and in vivo kinetic handling of nitrite in blood: effects ofvarying hemoglobin oxygen saturation,” Am. J. Physiol. Heart Circ.Physiol., vol. 293, pp. H1508-17, September 2007].

At normal venous blood oxygenation of 60%, the worst-case scenario forcapillary blood, approximately 100% conversion to met-Hb is achievedafter 60 seconds (using purified Hb) [R. Grubina, Z. Huang, S. Shiva, M.S. Joshi, I. Azarov, S. Basu, L. a Ringwood, A. Jiang, N. Hogg, D. B.Kim-Shapiro, and M. T. Gladwin, “Concerted nitric oxide formation andrelease from the simultaneous reactions of nitrite with deoxy- andoxyhemoglobin,” J. Biol. Chem., vol. 282, no. 17, pp. 12916-27, April2007]. Therefore, conversion efficiency and rate is not likely to be anissue.

Methemylation is therefore seen as a viable option for improving thereliability of COM-based microspectrometry. Using nitrite in largeexcess is posited to rapidly convert all of the Hb within cells and holdit at the maximum concentration longer than required for a COMexperiment.

In order to obtain consistently precise estimates of the MCH, it must beensured that a sufficient number of countable RBCs are present in anyCOM image.

The standard error of the mean of measurements drawn from a Gaussiandistribution ise=σ√{square root over (N)}  (12)

where σ is the standard deviation and N is the number of measurements.Assuming that m_(Hb,RBC) follows a Gaussian distribution between RBCs,this equation can be applied to the MCH measurement, where N is thenumber of RBCs sampled for the calculation of the MCH. Thus,“CV_(MCH,sampling)”, the CV of e_(MCH), is given by:

$\begin{matrix}{{CV}_{{MCH},{sampling}} = {{\frac{e_{MCH}}{MCH}*100} = {{\frac{\sigma_{{mHb},{RBC}}}{{MCH}\sqrt{N}}*100} = \frac{{CV}_{{mHb},{RBC}}}{\sqrt{N}}}}} & (13)\end{matrix}$

where σ_(mHb,RBC) is the standard deviation of the m_(Hb,RBC)measurements (MCH is the mean of these measurements). Solving for Nyields:

$\begin{matrix}{N = \left( \frac{{CV}_{{mHb},{RBC}}}{{CV}_{{MCH},{sampling}}} \right)^{2}} & (14)\end{matrix}$

Assuming a worst-case CV_(mHb,RBc) of 50% (which would be highlyclinically abnormal), to achieve an ideal value of CV_(MCH,sampling)<1%,making it essentially negligible with respect to a target CV_(MCH) of5%, at least 2000 RBCs need to be sampled, as calculated by substitutingthese two values into Equation (14).

At roughly 50 pixels per RBC and 8 million pixels in the field-of-viewof an 8 megapixel sensor with 1.1 μm pixel pitch, well over 10 000 RBCscan be included the field of view while still leaving most of the areaas background (which allows for more straightforward calculation of I₀).

Beer's law relies on several assumptions that do not necessarily holdtrue in Hb microspectrometry. This can lead to an overall bias,cell-to-cell variation, or person-to-person variation in MCHcalculations, the latter because blood from different persons can havedifferent characteristics. Bias is correctable, and the number of RBCscounted is so large that the error of the estimate of the meanm_(Hb,RBC) will be small, as described above. Person-to-person variationis therefore of the most concern, as this would weaken the correlationbetween MCH measured by COM and by current standard-of-practice (StP)

Beer's law assumes that there are no scattering or absorption eventscaused by materials in the blood film other than Hb itself. However,material inside or outside the RBC may also scatter or absorb. Theseevents may lead to underestimation of the MCH, the effect is likelysmall, as Hb has much higher absorbance than any other blood/diluentcomponent at the wavelengths in question [A. Airinei and A. Sadoveanu,“Spectrophotometric Analysis of the Blood Plasma,” Rom. J. Biophys.,vol. 16, no. 3, pp. 215-20, 2006][M. De, S. Rana, H. Akpinar, 0. R.Miranda, R. R. Arvizo, U. H. F. Bunz, and V. M. Rotello, “Sensing ofproteins in human serum using conjugates of nanoparticles and greenfluorescent protein,” Nat. Chem., vol. 1, no. September, pp. 461-5,2009].

Reference physiological “free” [Hb] (Hb concentration in the plasma) isas high as 5 μM [N. Na, J. Ouyang, Y. E. C. Taes, and J. R. Delanghe,“Serum free hemoglobin concentrations in healthy individuals are relatedto haptoglobin type,” Clin. Chem., vol. 51, no. 9, pp. 1754-5, September2005], while reference MCHC values are as low as 5000 μM [N. Beck,Diagnostic Hematology. London: Springer London, 2009], three orders ofmagnitude higher than the image background. Thus free plasma is normallynegligible in the MCH calculation. The assumption of negligible [Hb]outside RBCs may be false if significant hemolysis is present. Free [Hb]can rise to 10 μM during severe sepsis [M. Adamzik, T. Hamburger, F.Petrat, J. Peters, H. de Groot, and M. Hartmann, “Free hemoglobinconcentration in severe sepsis: methods of measurement and prediction ofoutcome,” Crit. Care, vol. 16, no. 4, July 2012] or 25 μM during asickle cell anemia crisis [D. J. Schaer, P. W. Buehler, A. I. Alayash,J. D. Belcher, and G. M. Vercellotti, “Hemolysis and free hemoglobinrevisited: exploring hemoglobin and hemin scavengers as a novel class oftherapeutic proteins,” Blood, vol. 121, no. 8, pp. 1276-84, February2013]. This, however, is still at least two orders of magnitude smallerthan the lowest levels of MCHC, so it is still negligible.

The general method for measuring the MCH on a drop blood using COM is asfollows:

-   -   1. A drop of blood is mixed and incubated briefly with an        appropriate diluent.    -   2. The blood mixture is injected into the specimen chamber,        forming a thin film between the COM sensor and a transparent        chamber lid.    -   3. Multiple images are acquired while light of a specific        wavelength is transmitted through the chamber lid to illuminate        the cells.    -   4. The images are processed and analyzed by a computer-vision        algorithm to extract the MCH.    -   5. The specimen chamber is cleaned.

To test the central hypothesis, several blood samples from differentindividuals were tested on both a single COM device and an StP machine.The data were compared using regression analysis to determine suitablecorrelation parameters, the coefficient of determination (R2), and theCV of the residuals.

Two diluents were prepared for the experiments, to be mixed with bloodat a 3:1 diluent:blood ratio. Diluent N, which included NaNO2, containedthe following components dissolved in distilled water:

1. 112.3 mM NaNO2

2. 6.5 mM Brilliant Cresyl Blue stain (Sigma, 860867)

3. 21.3 mM Disodium EDTA (EM Science, EX0539-1)

4. 5.87 mM HEPES (Sigma, H4034)

5. 5.57 mM KCl

6. 7.36 mM NaCl

Diluent xN, which excluded NaNO2, contained the following componentsdissolved in distilled water:

1. 6.5 mM Brilliant Cresyl Blue stain (1% w/w)

2. 21.3 mM Disodium dihydrate EDTA

3. 5.87 mM HEPES

4. 5.57 mM KCl

5. 118.6 mM NaCl

The key component of Diluent N diluent was NaNO2, used to convert oxy-Hband deoxy-Hb to met-Hb. Diluents N and xN were identical except for thereplacement in Diluent xN of NaNO2 with a molar equivalent of NaCl. TheBrilliant Cresyl Blue stain was included for experiments that are beyondthe scope of this research. The diluent was designed to fulfill severalfunctions to ensure that the cells maintained their health and form:

-   -   1. Isotonicity, to maintain cell shape and health, was achieved        using appropriate concentrations of NaCl, KCl, and NaNO2 in the        diluents.    -   2. Anticoagulation prevents blood cells from aggregating        irreversibly, which makes counting them difficult.        Ethylenediaminetetraacetic acid (EDTA) was included as an        anticoagulant.    -   3. The diluent was buffered at physiological pH (˜7.4) using the        HEPES buffer.

In some cases the diluent is chosen to achieve sphering of the red bloodcells. It may be advantageous if the spacer (chamber height) is smallenough (<3 μm) so that the resulting spherical red blood cells arecompressed into a more-or-less cylindrical shape by the loweredchamber-lid, to provide an approximately uniform path length for thecollimated light and to simplify the determination of cell volume. Thesphering agent can comprise inclusion of a surfactant such as sodiumdodecyl sulfonate or hexadecyl trimethyl ammonium chloride. (See USpatent application publication 2009/0258338).

Diluents were used to dilute a small blood sample at a 3:1 diluent:bloodratio. A thin film of this mixture was then formed by placing a dropbetween a glass slide and a glass coverslip. This film was examinedusing transmission microscopy to ensure that the cells were not damagedby the diluent. Cells using diluents N and xN diluents appeared verysimilar to cells in blood diluted using other formulations that met therequirements described above. In an acceptable diluent formulation, RBCsappear quite circular, with few having bulges or wrinkles. Also, almostall RBCs have light centers surrounded by a darker band. This indicatesbiconcavity, since the thinner cell center absorbs less light. Further,lack of coagulation is indicated by the individual cells appearingfairly evenly distributed and without stacking.

The COM sensor used was an Omnivision OV8850, with 1.1 μm square pixels.Bayer color filters were removed by reactive-ion etching.

A transparent chamber lid was used to form the thin blood film on theCOM sensor. A drop of blood:diluent mixture was placed on the sensor,and the chamber lid was gently lowered onto the sensor surface to aheight of ˜3.5 μm, with the height set using a spacer. This forced mostof the blood mixture out the sides, leaving a small film of fluidremaining in the gap to be imaged.

The chamber height was not measured precisely. However, typical RBC havea maximum thickness of 2.6 μm, so even if the chamber height varied by20%, the chamber lid should not squash any RBCs. This height shouldcause the RBCs to lie flat though, as they have typical diameters 7.5 μm[Y. Park, C. a Best, T. Auth, N. S. Gov, S. a Safran, G. Popescu, S.Suresh, and M. S. Feld, “Metabolic remodeling of the human red bloodcell membrane,” PNAS, vol. 107, no. 4, pp. 1289-94, January 2010]. Whenthe chamber lid was well seated, RBC stacking (rouleaux) was notobserved.

The illuminator for the blood images consisted of two LEDs: a 405 nm LED(Mouser Electronics, 749-UV3TZ-405-15), a 415 nm LED (Lumex,SSL-LXT046UV3C).

Experiment 1: Using Diluent N (nitrite-included) and 405 nm light, bloodsamples from 15 different individuals were each tested once on the StP.These 15 samples were tested between two and four times each using COM.

Experiment 2: The final six samples used in Experiment 1 were alsotested using COM and Diluent xN (nitrite-free) with 415 nm illumination,with between one and four COM tests per sample. For these six samples,the Experiment 2 tests were performed prior to the Experiment 1 testsbecause the former are likely to be more sensitive to oxygencontamination.

For the MCH calculations, all images were processed identically. Fourviolet images were averaged together to reduce noise, and the 20-frameaveraged 6 ms-exposure darkframe was subtracted, yielding a singlemaster image. The computer-vision algorithm consisted of three steps:

1. Estimate the background value for every pixel in the image

2. Segment regions containing exactly one RBC (image segmentation)

3. Calculate the MCH using Equation (8)

In each RBC segmentation, the background level calculated for each pixelwas assigned to I0,np in Equation (8). For the image segmentation ofstep 2, a COM image was first thresholded to produce a binary mask.Objects greater than 1.5× or less than 0.5× the mean RBC size wereeliminated. This eliminated the vast majority of noise, artifacts, RBCclusters, and occasional pixels from non-RBC objects. The remainingobjects became the RBC segmentations for the MCH calculation. Step 3took the intensity values for the pixels in each RBC segmentation andscanned over them, applying the MCH equations described above. Equation(11) was used with Diluent N, and Equation (9) was used for Diluent xN.

Data acquisition and analysis in comparison with StP showed that theresults of the Diluent xN tests and the corresponding Diluent N testswere somewhat similar. Both met the CVMCH, target requirement, withsimilar CVMCH's of 2.27% and 3.38% respectively. For these subjects, thecorrelation between COM and StP measurements was very strong for bothDiluent N and xN, with R2 values of 0.96 and 0.98 respectively.

Variation in MCHCOM measurements depends on several factors, one ofwhich is RBC shape, presumably due to violations of the Beer's lawassumptions discussed above. Thus measurements of MCH, as well as ofMCV, can be improved by converting the RBCs from dicoid to sphericalshape [Kim Y R, Ornstein L: Isovolumetric sphering of erythrocytes formore accurate and precise cell volume measurement by flow cytometry.Cytometry 4:419, 1983] and various US patents [e.g., Fan et al., 1997U.S. Pat. No. 5,633,167 and patents cited therein]. If the chamber(i.e., spacer) height is less than the diameter of the sphered RBCs,they will be compressed into cylinders of uniform thickness.

Other implementations are within the scope of the following claims. Forexample, the described approach is not restricted to measurements ofhemoglobin in red blood cells, but can be used to measure any substancethat has a distinctive absorbance spectrum, in any cell or particle (so,for example in principle, for monitoring production of biopharmaceuticalcompounds by genetically engineered yeast cells).

What is claimed is:
 1. A method comprising: placing a fluid sample on asurface of a contact optical microscopy sensor, the fluid samplecomprising particulate matter that includes an analyte to be measured,the analyte having a distinctive absorption spectrum; forming a film ofthe fluid sample between a transparent chamber lid and the surface ofthe contact optical microscopy sensor by moving the transparent chamberlid to a predetermined distance from the surface of the contact opticalmicroscopy sensor, the distance determined by a spacer, the moving ofthe transparent lid to the predetermined distance causing theparticulate matter of the fluid sample to be compressed into a shape toprovide a uniform path length for light passing through the particulatematter; acquiring one or more images of the fluid sample based onilluminating at least a portion of the film of the fluid sample usinglight of a predetermined wavelength, processing the acquired one or moreimages of the fluid sample; and determining a mean amount of the analytebased on the processing of the one or more images of the fluid sample bya processor.
 2. The method of claim 1, in which the particulate matterof the fluid sample is compressed into a cylindrical shape.
 3. Themethod of claim 1, wherein a magnitude of the predetermined distance issuch that lowering of the transparent chamber lid onto the surface ofthe contact optical microscopy sensor (i) constrains the particulatematter of the fluid sample to lie parallel to the surface of the contactoptical microscopy sensor, and (ii) does not result in structural damageto the particulate matter of the fluid sample.
 4. The method of claim 1,wherein processing the one or more acquired images comprises: segmentingone or more regions within the respective acquired images that eachcontain exactly one particle; and calculating the mean amount of theanalyte based on the segmented one or more regions within the respectiveacquired images.
 5. The method of claim 1, wherein forming the filmcomprises: placing the fluid sample on the surface of the contactoptical microscopy sensor; and enabling the fluid sample placed on thesurface of a contact optical microscopy sensor to settle.
 6. The methodof claim 1, wherein the acquired one or more images include at least astatistically significant number of particles in the fluid sample. 7.The method of claim 1, wherein the predetermined wavelength is based onextinction coefficients of wavelengths within an absorbance band of theanalyte.
 8. The method of claim 1, wherein the analyte compriseshemoglobin.
 9. The method of claim 8, wherein the fluid sample comprisesoxygenated hemoglobin and deoxygenated hemoglobin, and the methodfurther comprises converting both types of hemoglobin to a third type ofhemoglobin.
 10. The method of claim 1, comprising mixing the fluidsample with a diluent to generate a diluted sample.
 11. The method ofclaim 10, wherein the diluent comprises a nitrite.
 12. The method ofclaim 10, wherein the fluid sample comprises blood, and whereingenerating a diluted sample comprises mixing the sample of blood with adiluent such that the mixing results in sphering of red bloods cellswithin the diluted sample.
 13. The method of claim 10, wherein the fluidsample comprises blood, and wherein the generated diluted sample has atleast two of the following properties: has an isotonicity that issubstantially equal to an isotonicity of red blood cells; hascoagulation properties such that the generated diluted sample is lesslikely to coagulate compared to coagulation properties of red bloodcells; and maintains a predetermined pH level of the generated dilutedsample.
 14. The method of claim 1, wherein the mean amount of theanalyte is an average mass of hemoglobin per red blood cell in the fluidsample.
 15. The method of claim 1, comprising determining the meanamount of the analyte with a coefficient of variation of less than 1percent.
 16. The method of claim 1, in which processing the acquired oneor more images of the fluid sample comprises performing a calculationbased on a measured intensity of the light of the predeterminedwavelength at the surface of the contact optical microscopy sensor, thecalculation including an assumption that the path length for lightpassing through the particulate matter is uniform within each pixel ofthe contact optical microscopy sensor.